Fine-tuning
In one line
Fine-tuning takes an already pretrained LLM and trains it further on a narrower dataset to specialise it for a domain, task or voice — the most common path for adapting an LLM to your own data.
Going deeper
Fine-tuning takes a pretrained LLM and trains it further on a smaller, task-specific dataset — say, your customer support transcripts or your brand voice. If pretraining is the general-education degree, fine-tuning is the on-the-job training.
The marketing appeal is obvious: 'make the AI sound like us, with our answers'. The trade-off is cost and operational overhead, which is why many teams now prefer to leave the model alone and inject knowledge via RAG instead.
Honestly, in most marketing use cases you can get by without fine-tuning. A solid system prompt, a few-shot examples and RAG cover the bulk of it. Fine-tuning earns its keep when you have plenty of labelled data and need very consistent, narrow outputs.
Related terms
Pretraining
Pretraining is the initial stage where an LLM is trained on huge amounts of text to learn general language capability — the step where the model absorbs most of its 'world knowledge'.
LLMRLHF
RLHF (Reinforcement Learning from Human Feedback) trains an LLM using human preference signals so it produces more helpful, safer responses — the recipe behind the leap in ChatGPT-style quality.
LLMAI Alignment
AI alignment is the field — and the practical work — of making AI systems behave in line with human intent, values and safety constraints.
LLMRAG
RAG (Retrieval-Augmented Generation) lets an LLM fetch external documents at answer time and ground its response in them — the technique behind ChatGPT Search, Perplexity and most AI search products.
LLMOpen-weight Model
An open-weight model is an LLM whose weights are publicly released so anyone can download and run it on their own infrastructure — Llama, Mistral and Qwen are the best-known examples.
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